# Machine Learning Course - Readme ## Course Overview This repository contains materials and code for the Machine Learning course I attended on GeeksForGeeks. The course covered various topics related to Machine Learning, including but not limited to: - Introduction to Machine Learning - Supervised Learning - Unsupervised Learning - Deep Learning - Model Evaluation and Selection - Feature Engineering - Data Preprocessing - etc. ## Course Contents - **Module 1:** Introduction to Machine Learning - Introduction to ML and its applications - Types of Machine Learning algorithms - Setting up the development environment - **Module 2:** Supervised Learning - Linear Regression - Logistic Regression - Decision Trees and Random Forests - Support Vector Machines (SVM) - Model evaluation metrics - **Module 3:** Unsupervised Learning - Clustering algorithms (K-Means, DBSCAN, etc.) - Dimensionality reduction techniques (PCA, t-SNE, etc.) - Anomaly detection - **Module 4:** Deep Learning - Neural Networks and Architecture - Activation Functions - Backpropagation - Convolutional Neural Networks (CNNs) - Recurrent Neural Networks (RNNs) - **Module 5:** Model Evaluation and Selection - Cross-Validation - Hyperparameter tuning - Bias-Variance Tradeoff - Ensemble methods - **Module 6:** Feature Engineering and Data Preprocessing - Feature selection - Handling missing data - Feature scaling and normalization - One-Hot encoding and feature transformation ## How to Use This Repository 1. Clone the repository to your local machine using the following command: 2. Install the required dependencies using `pip` or `conda`. Use a virtual environment for better isolation. 3. Navigate to the respective module directories to access the code and materials for each topic. ## Additional Notes - The code and materials in this repository are for educational purposes only and may not cover all aspects of Machine Learning. - If you encounter any issues or have any questions, feel free to open an issue in this repository. - Please make sure to adhere to the GeeksForGeeks terms and conditions and use the materials responsibly. ## Acknowledgments I would like to express my gratitude to the instructors and mentors at GeeksForGeeks for providing valuable insights and knowledge during this Machine Learning course. ## License This project is licensed under the [MIT License](LICENSE).